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Enhance training script with argument parsing and logging #35

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Enhance training script with argument parsing and logging #35

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51 changes: 32 additions & 19 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import os
import warnings
import sys
import argparse

import pandas as pd
import numpy as np
Expand All @@ -14,7 +15,7 @@

import mlflow
import mlflow.sklearn

from mlflow.models.signature import infer_signature

def eval_metrics(actual, pred):
rmse = np.sqrt(mean_squared_error(actual, pred))
Expand All @@ -28,9 +29,19 @@ def eval_metrics(actual, pred):
warnings.filterwarnings("ignore")
np.random.seed(40)

# Robust Argument Parsing
parser = argparse.ArgumentParser(description="ElasticNet Wine Quality Model")
parser.add_argument("--alpha", type=float, default=0.5, help="Alpha learning rate")
parser.add_argument("--l1-ratio", type=float, default=0.5, help="L1 regularization ratio")
args = parser.parse_args()

# Read the wine-quality csv file (make sure you're running this from the root of MLflow!)
wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv")
data = pd.read_csv(wine_path)
try:
data = pd.read_csv(wine_path)
except Exception as e:
print(f"Unable to load data. Error: {e}")
sys.exit(1)

# Split the data into training and test sets. (0.75, 0.25) split.
train, test = train_test_split(data)
Expand All @@ -41,26 +52,28 @@ def eval_metrics(actual, pred):
train_y = train[["quality"]]
test_y = test[["quality"]]

alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5

# Enable Autologging
mlflow.sklearn.autolog()
with mlflow.start_run():
lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
lr = ElasticNet(alpha=args.alpha, l1_ratio=args.l1_ratio, random_state=42)
lr.fit(train_x, train_y)

predicted_qualities = lr.predict(test_x)

(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
print(" RMSE: %s" % rmse)
print(" MAE: %s" % mae)
print(" R2: %s" % r2)

mlflow.log_param("alpha", alpha)
mlflow.log_param("l1_ratio", l1_ratio)
mlflow.log_metric("rmse", rmse)
mlflow.log_metric("r2", r2)
mlflow.log_metric("mae", mae)

mlflow.sklearn.log_model(lr, "model")
print(f"Elasticnet model (alpha={args.alpha:f}, l1_ratio={args.l1_ratio:f}):")
print(f" RMSE: {rmse}")
print(f" MAE: {mae}")
print(f" R2: {r2}")

# Log Model with Signature and Input Example
signature = infer_signature(train_x, predicted_qualities)
input_example = train_x.iloc[:5]

mlflow.sklearn.log_model(
lr,
"model",
signature=signature,
input_example=input_example
)